Methods and systems for determining dynamic hyperinflation

ABSTRACT

This invention provides methods and systems for non-invasively determining the presence (and amount) or absence of dynamic hyperinflation in a subject. The invention is based on a novel combination of respiratory parameters that can be measured in a way that is non-invasive and unobtrusive to the subject. Dynamic hyperinflation is often a significant factor in the quality of life of patients suffering from a variety of obstructive pulmonary diseases, and this invention permits simple, routine tracking and management of dynamic hyperinflation in affected patients.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of prior U.S. provisional application 60/682,876 filed May 20, 2005.

1. FIELD OF THE INVENTION

The present invention relates to determination of pulmonary parameters of individuals, especially pulmonary parameters of patients with obstructive pulmonary diseases. More particularly, this invention provides systems and methods that measure dynamic hyperinflation using methods that require little if any patient attention.

2. BACKGROUND OF THE INVENTION

Chronic obstructive pulmonary disease (COPD) and other diseases with a similar physiological defects (e.g., acute and chronic asthma) are of considerable clinical interest since they are important worldwide causes of morbidity and mortality. Cardinal symptoms of these diseases include sensations of dyspnea or breathlessness as well as other respiratory discomforts. These occur on exertion, and in advanced disease also at rest. Briefly, these symptoms are due to progressive loss of lung volume available for active breathing as the lung becomes filled with more and more air trapped (“hyperinflation”) behind airways that have increasing expiratory flow limitations. The airway expiratory flow limitations result from the pathology underlying these diseases that, for example, causes blockages within airways (e.g., by increased mucus) or partial airway collapse (e.g., by decreased tethering due to parenchymal destruction). The increase in lung volume changes the pressure-volume relationship of the chest-wall, reducing the efficiency of the respiratory musculature.

Pathologically, COPD is a heterogeneous disorder characterized by expiratory flow limitations usually due to narrow, easily collapsed airways. When arising from emphysema or chronic bronchitis, parenchymal and vascular destruction reduces lung recoil and airway tethering leading to expiratory collapse of small and large airways. Acute and chronic asthma, along with chronic bronchitis, can also cause expiratory flow limitation by airway narrowing due to bronchial hypertrophy, bronchial spasm, and increased viscid secretions into the bronchi. Pulmonary diseases characterized by prominent expiratory air flow limitations are generically referred to herein as “obstructive pulmonary diseases” (OPD).

It is common during the course of these diseases that periods of acutely increased hyperinflation (“dynamic hyperinflation”) are superimposed on the chronic underlying and often slowly progressive hyperinflation. Dynamic hyperinflation (abbreviated as “DH”) is associated with periods of increased drive to breathe which can be due to exercise (“exercise dyspnea”), excitement, pulmonary infections, waking in the morning, and numerous other factors. The additional hyperinflation caused by DH can even further decrease lung capacity available for active breathing, and therefore can be a substantial factor in the experience of patients with COPD and similar diseases, negatively impacting their functional capacity and quality of life.

Thus, it is clinically advantageous to track and treat episodes of DH to the extent possible. In the prior art, DH has usually been tracked by serial measurements of inspiratory capacity (abbreviated herein as “IC”) requiring a patient to perform a specific breathing maneuver at rest while, for example, breathing into a spirometer or breathing while inside a calibrated pneumo-tachographic chamber. The specific maneuver requires that the patient must, first, repeatedly inspire and expire in a relaxed manner, and then must inspire maximally and resume normal breathing. The IC is difference between the last inspiratory volume and the last tidal expiratory volume. Preferably, this maneuver is repeated until two or more consistent IC values are obtained.

This requirements of this measurement technique can distort IC measurements, and thus confound identification and measurement of DH also. A patient must interrupt whatever they were doing and then consciously attend to and perform a specific breathing sequence while using instrumentation that is at best cumbersome. The large inspiration required by IC measurement can be unpleasant, and may actually trigger a period of hyperinflation. Performance of the IC maneuver is especially intrusive during exercise, and measurements of DH precipitated by exercise (“exercise dyspnea”) are likely to be more confounded than are measurement of other forms of DH.

The prior art lacks systems and methods for measuring DH that require little or no attention by a patient. Such methods and system would be useful for, e.g., assessing and managing COPD and other lung diseases.

A number of references are cited herein, the entire disclosures of which are incorporated herein, in their entirety, by reference for all purposes. Further, none of these references, regardless of how characterized above, is admitted as prior to the invention of the subject matter claimed herein.

3. SUMMARY OF THE INVENTION

The objects of this invention include methods and systems that assess dynamic hyperinflation (“DH”) in a patient unobtrusively, that is with little or no attention by the patient, and also preferably non-invasively, that is permitting the patient to perform normal daily activities. Tracking and managing DH using this invention can be useful in improving the quality of life of patients with obstructive pulmonary diseases (“OPD”), because DH can be a substantial factor in their disease experience. OPD patients include patients with obstructive pulmonary diseases, e.g., COPD, chronic bronchitis, emphysema, chronic or acute asthma, and other diseases with similar physiological effects.

This invention is based on the inventor's discovery that the presence or absence of DH and its amount (e.g., the volume of dynamically retained air) can be assessed by a novel combination of respiratory parameters. In particular, patterns of changes in the median rib cage contribution to tidal volume (M % RC when measured in percent) and the median absolute value of changes in end-expiratory lung volume (MqdEELV) can reliably detect DH. During periods of increased respiratory demand, e.g., during exercise, these parameters often increase together in normal patients who do not have DH. In other normal patients, one of these parameters may change while the other does not change, or neither of these parameters may change. In contrast, OPD patients experiencing DH during periods of increased respiratory demand demonstrate a different and unique pattern in which MqdEELV increases while M % RC decreases. These different patterns of changes of MqdEELV and M % RC reliably discriminate patients who experience DH from those who do not. As used here, a parameter “changes” (or “increases” or “decreases”) if its values, or if an average, or median, or mode, or other statistical measure of its values, observed in two conditions differ to a statistically meaningful degree.

This invention provides methods and programmed computer systems that implement this discovery. These methods and systems receive respiratory data sufficient to determine MqdEELV and M % RC, process this data, and output assessments of the presence or absence of DH and optionally of its amount. Various preferred embodiments of this inventor are more or less specifically directed to different patient measurement environments, e.g., hospital environments, clinical environments, ambulatory environments, laboratory environments, and the like. Specifically, the various embodiments are adapted to accept respiratory data from the different respiratory sensors found in these different environments, and are implemented on the various types of computers also found in these different environments, from computers with limited portability to portable computers that can be carried by a patient.

One preferred embodiment is directed to ambulatory patients. As used herein “ambulatory environment” (or “ambulatory”), is taken to means an environment that permits patients to engage their normal daily activities in a substantially unconstrained manner. In this embodiment, respiratory data is measured using sensors configured on and/or carried by a comfortable wearable item. Preferred respiratory sensors measure sizes of the patient's torso at one or more levels, e.g., at a rib cage level and/or and abdominal level, using plethysmographic technologies, particularly inductive plethysmographic technologies. Data is processed either by portable processing devices that can be carried by the patient or by remote computer systems at least to extract tidal volume (V_(T)) from sensor data and then to determine MqdEELV and M % RC from V_(T) data. DH is then assessed in dependence one the latter two parameters. Processed and/or raw respiratory data is preferably transmitted from local devices to remote systems using means that permit a patient to carry out their normal activities with little or no significant constraint. For example, data can be transmitted wirelessly, or physically transferred on computer readable media.

Specifically, “plethysmography” and its derivative words, as used herein, refer to an externally-determined (non-invasive) measurement of a size of a body part. Also, “inductive plethysmography”, as used herein, is a plethysmographic measurement based on determination of an inductance or a mutual inductance of conductive elements arranged on the body part. A “plethysmographic signal” is a signal generated by plethysmography, and usually by inductive plethysmography. The part of the body measured by plethysmography may include, singly or in combination, one or more portions of the chest, abdomen, neck, arm, or the like.

The present invention also includes computer readable mediums, both for long term storage and for portable storage, which are configured with encoded instructions for causing a processor to perform the methods of this invention and/or with raw or processed data used by these methods.

Specific embodiments of this invention will be appreciated from the following detailed descriptions, appended claims, and attached figures.

4. BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be understood more fully by reference to the following detailed description of preferred embodiments of the present invention, illustrative examples of specific embodiments of the invention, and the appended figures in which:

FIGS. 1A1-1A4 illustrate aspects or respiratory anatomy;

FIGS. 1B-D illustrate aspects of respiratory function;

FIG. 2 illustrates calibrated respiratory data;

FIG. 3 illustrates the measurement of EELV in this invention;

FIG. 4 illustrates respiratory data from a normal patient;

FIG. 5 illustrates respiratory data from a patient with OPD;

FIG. 6 illustrates ambulatory monitoring devices; and

FIG. 7 illustrates methods of this invention.

5. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following, and in the application as a whole, headings are used for clarity and convenience only and without any intent to thereby alter or narrow the scope of the invention.

Dynamic Hyperinflation

This subsection provides brief descriptions of known aspects of pulmonary functioning that are useful for describing and supporting the present invention, in particular, aspects of respiratory mechanics and of respiratory volumes.

FIGS. 1A1-4 schematically illustrate relevant aspects of respiratory anatomy and mechanics. FIGS. 1A1 and 1A2 illustrate a side view and a cross section of a rib cage in inspiration. FIGS. 1A3 and 1A4 illustrate a side view and a cross section of a rib cage in expiration. Respiratory muscles acting directly on the rib cage are not illustrated; only diaphragm 23 is illustrated. Referring first to FIGS. 1A1 and 1A2, the respiratory muscles during inspiration act to lift and expand rib cage to position 11 and to lower the diaphragm to position 15. Referring now to FIGS. 1A3 and 1A4, during expiration, the respiratory muscles relax, and tissue elasticity and passive recoil allow the rib cage to contract to position 19 and the diaphragm to rise to position 21. Accordingly, measures of rib cage size (RC) increase during inspiration and decrease during expiration. This can be appreciated from the relation of the rib cage to equal length arrows 13 (FIGS. 1A1 and 1A3). Similarly, measures of abdomen (AB) size increase during inspiration and decrease during expiration. This can also be appreciated from the relation of the abdomen to equal length arrows 17 (FIGS. 1A2 and 1A4).

RC and AB size measurements (or either alone) can be linearly combined according to a two compartment breathing model in order to determine the various lung volumes, e.g., tidal volume, to within 5-10% of these volumes determined using a spirometer (a current measurement standard). Furthermore, comparing measurements of changes in rib cage and abdominal sizes to determined lung volumes, it can be determined how much of an individuals is due to rib cage motion and how much is due to diaphragmatic motion.

It is also known that, when inspirations and expirations occur at chest volumes in the central parts of their volume ranges, approximately midway from their minima to their maxima, a given change in lung volume requires a linear or proportionate amount of respiratory muscle effort (working against the elasticity of chest wall and lung). That is, in these volume ranges, the lung compliance is approximately constant so that minute ventilation (VE) increases approximately linearly and proportionately with respiratory muscle work. However, it can be appreciated from FIGS. 1A1 and 1A2 that when breathing occurs with chest and lung volumes in the upper part of their ranges, near their maxima, each breath requires that an already expanded rib cage 11 must be further expanded and an already lowered diaphragm 15 must be further lowered. This is mechanically and elastically disadvantageous; and each breath requires a non-linearly and disproportionately greater amount of respiratory muscle effort. Because of this decreasing lung compliance, greater and greater efforts and required to increase VE.

Next, FIGS. 1B-C illustrate schematically (and not to scale) relevant aspects of functional lung volumes. Referring first to FIG. 1B, total lung capacity (TLC) is the total volume of air in the lung; residual volume (RV) is the total volume of air remaining in the lung after a maximum expiratory effort; and function residual capacity (FRC) is the total volume of air remaining in the lung after a tidal expiration. (Alternately, TLC is often defined to include dead volume where there is no significant air exchange.) Measurement of these volumes commonly requires cumbersome techniques, for example, body plethysmography or gas dilution measurements.

The remaining lung volumes are more easily measured during routine breathing. Vital capacity (VC) is the expiratory volume from a maximal inspiration down to a maximal expiration. Normal breathing defines tidal end inspiratory lung volume (EILV) and tidal end expiratory lung volume (EELV), and the difference of these volumes defines tidal volume (V_(T)). Inspiratory capacity (IC), inspiratory reserve volume (IRV), and expiratory reserve volume can then be determined from VC and V_(T) (either resting or exercise) as illustrated. In particular, IC is the inspiratory volume from a regular expiration up to a maximal inspiration, and will vary proportionately with the EELV.

FIG. 1C illustrates a normal subject's response to increased respiratory demand, such as occurs during exercise. The principal response is to increase V_(T); the secondary response is to increase respiratory frequency, but usually only at high levels of respiratory demand. Because normal subjects have substantial IC and IRV, V_(T) is easily increased by simply taking deeper inspirations without changing the EELV. Healthy subjects may also demonstrate a decreasing EELV as permitted by their VC and ERV. These inspiratory volume increases (and also expiratory volume increases) occur in the mid-range of TLC, e.g., between 20% and 60% of TLC. In this range, the respiratory system's compliance is largely constant, and increased respiratory effort leads to substantially linear and proportional increases in V_(T) and VE.

COPD, and other lung diseases leading to similar physiological defects, can dramatically compromise the normal respiratory response to increased respiratory demand. FIG. 1D illustrates a patient with advanced COPD. This patent's ventilatory reserve volumes are dramatically decreased by air flow limitations, incomplete expiratory lung, and static trapping of air not expired in lung segments with little of no ventilation in such patients leads. At rest (i.e. for ventilation near 10 L/min in FIG. 1D), approximately 70% of the lung is no longer available for active respiration and gas exchange. Only approximately 20% of TLC remains available as ventilatory reserve volumes, VC, IC, and ERV, which can be used to increase V_(T) when needed. Also, as the active and ventilated lung volumes are displaced upward and closer to total lung capacity, respiratory compliance decreases. Increases in VE and V_(T) then require greater and greater respiratory effort; and breathing becomes more and more tiring. OPD also increases to work of breathing also by increasing airway resistance. The patient illustrated in FIG. 1D therefore loses the normal subject's capability to substantially increase VE in response to respiratory demand (i.e., exercise).

In addition to being chronic, air trapping can also be acutely exacerbated and increased by various precipitating factors, e.g., a patient's attempts to increase VE as might occur during exercise. This can be seen by comparing the course of EELV and IC in FIGS. 1C and 1D. In a normal subject, FIG. 1C illustrates that EELV and IC are not substantially altered when VE increases. Thus, ventilatory residual volumes, or the lung volumes available for increased ventilation, are not compromised and remain available for increasing V_(T). However, FIG. 1D illustrates that, in an OPD patient, EELV can further increase and IC can further decrease during even modest increases in VE. Here, ventilatory residual volumes are compromised and are not available for increasing V_(T). In fact, it can be seen that the possible increase in V_(T) is quite limited. However, it is also apparent from FIG. 1D that, if EELV and IC were substantially constant as VE increased, the patient's ventilatory reserve volumes, although already quite limited, are at least not further compromised and can be fully used to increase V_(T) and thus VE. This additional effect of OPD, though to be caused by dynamically increased air trapping, is known as “dynamic hyperinflation” (DH). DH is dynamic because after precipitating factors cease, lung volumes return to their previous values.

The occurrence and extent of DH is variable. Determinants of DH include: baseline lung hyperinflation, expiratory flow limitation, ventilatory demand, and breathing pattern. The occurrence of DH is also variable. It be precipitated by different causes in different patients, for example, by exercise, by lung infection, by cold, by allergens and so forth. Although air trapping can range from 0.1 L or less, to 0.3 L, and to ≧1.0 L, absolute volumes must be compared to a patient's remaining lung function. Only 0.1-0.2 L of additional air-trapping can significantly compromise an individual already breathing with virtually no ventilatory reserve.

Thus DH acts like a further worsening of a patient's underlying disease because it further decreasing reserve volumes just when the patient needs them in order to increase ventilation. In these situations, notably during exercise, OPD patients can experiences increased sensations of dyspnea, breathlessness, and other discomforts. Therefore, tracking and managing DH in OPD patients can help preserve their functional capacity and quality of life.

Determination of Dynamic Hyperinflation

This subsection describes this invention's novel techniques and methods for non-invasively and unobtrusively determining dynamic hyperinflation. These are based on the discovery that the presence or absence of DH and an indication of its amount (volume) can be reliably determined by a finding specific pattern of joint changes in two specific respiratory parameters occurring during exercise or after other precipitating factor. On the other hand, DH cannot be reliably determined from only one of these parameters in isolation from the other.

The two significant parameters are known as “median % rib cage” (“M % RC”) and “median cumulative change in end expiratory lung volume” (“MqdEELV”). These parameters and their measurement are now described. First, M % RC is a parameter available for each breath and measures the relative portion of a breath that is due to expansion and contraction of the rib cage. The remaining portion of the breath is due, as explained above, to contraction and relaxation of the diaphragm. FIG. 2 illustrates exemplary data. Graph 45 represents the tidal volumes (V_(T)) a series of breaths, each breath has a rising inspiratory portion and falling expiratory portion. Graph 47 represents concurrent relative changes in rib cage volumes, and graph 49 concurrent relative changes in abdomen volumes. For each breath, the % RC (percent RC) is the ratio of the amplitude in graph 47 to the corresponding amplitude in graph 45.

M % RC can be determined from respiratory measurements including tidal volume, or its equivalent, and rib cage size, or its equivalent, and is a weighted and/or normalized ratio of changes in rib cage size to the tidal volume. In preferred embodiments, respiratory data includes non-invasive, unobtrusive, moment-by-moment measurements of a rib cage size, known as RC, and an abdominal size, known as AB. Then, suitable weights α and β can be chosen so that tidal volume can be reliably determined from the formula: V _(T) =α*AB+β*RC  (1) The % RC (percent RC) is then:

$\begin{matrix} {{\%\mspace{11mu}{RC}} = \frac{\beta*{RC}}{V_{T}}} & (2) \end{matrix}$ M % RC is a statistical median of % RC determined for breaths occurring during sequential 30 sec., or 1 min, or 2 min., or other periods. In other embodiments, V_(T) can be alternately determined from a linear or non-linear function of AB and RC, and an alternate statistical measure, e.g., an average, can be used to represent values of % RC during sequential periods.

Next, finding MqdEELV requires prior determination of changes in EELV. Patterns of change in EELV are sufficient; the methods of this invention do not require absolute values of EELV. In preferred embodiments, changes in EELV are determined breath-by-breath by comparing the inspiratory volume of each breath to its expiratory volume. FIG. 3 illustrates a preferred method. Graph 31 schematically (not to scale) represents V_(T) and includes four illustrative breaths, breaths 36, 40 and 42 being specifically identified. Breath 36 has inspiratory volume 35, which is measured from the end expiration of the previous breath to peak inspiration of breath 36, and expiratory volume 37, which is measured from peak inspiration of breath 36 to end expiration of breath 36. Since inspiratory volume 35 is greater than expiratory volume 37, breath 36 has caused EELV to increase by the difference in these volumes. EELV is represented by graph 33, and this graph depicts the EELV increase due to breath 36 by step 39. Breath 40 is similar: inspiratory volume 39 is greater than expiratory volume 41; and graph 33 represents this EELV increase by step 43.

Accordingly, graph 31 of EELV steps up by amount 39 at breath 36, by amount 43 at breath 40, and by a further amount at breath 42. Over the course of these three breaths, EELV cumulatively stepped up by amount 45. In actual respiratory data, EELV can both increase and decrease, and the cumulative change in EELV for a period of time cumulates all increases and decreases during that period. Finally, MqdEELV is the statistical median of the absolute value of a number of cumulative changes in EELV determinations made during sequential 30 sec., or 1 min, or 2 min., or other periods. In other embodiments, changes in MqdEELV can be determined by linear or non-linear combinations of inspiratory and expiratory volumes from two or more breaths; EELV changes can be cumulated by running averages and the like; and alternate statistical means, e.g., an average, can be used to characterize changes in EELV during sequential time periods.

Once these parameters are determined, the methods of this invention identify a patient who dynamically traps air in response to an inciting event (e.g., exercise, infection, etc.) because of an increasing MqdEELV together with a decreasing M % RC. Such patients are referred to herein as “+DH”. In patients who do not dynamically trap air, referred to as −DH, MqdEELV and M % RC do not exhibit this pattern. In such patients, either MqdEELV decreases, or both MqdEELV and M % RC increase together.

FIGS. 4 and 5 illustrate these two patterns. In these figures, median values are determined for a time equal to approximately one and one-half time the vertical grid line spacing. FIG. 4 illustrates a healthy, −DH, subject who exhibits a normal pattern of joint changes in MqdEELV and M % RC during exercise. This patient has a significant exercise capability as evidence by a heart rate increase from approximately 70 beats per minute (bpm) to over 170 bpm. During the exercise period, the MqdEELV is stable, while the M % RC increases a small amount, from approximately 40% to approximately 50%. This pattern reflects a normal exercise response in which minute ventilation is increased by increasing tidal volume using rib cage muscles and the diaphragm in approximately equal proportions. No air trapping is evident. Another common normal pattern is a relatively small decrease in MqdEELV, because of somewhat large expirations, together with a relatively small increase in M % RC, because of relatively increased use of rib cage muscles. Also normals can exhibit a small increase in MqdEELV with changes in M % RC that are either not significant or are decreases.

FIG. 5 illustrates a subject with an obstructive pulmonary disease (here, COPD). First, this subject's exercise tolerance is decreased due to limitation in their ability to increase their minute ventilation. This is evidenced here by the only approximately 20 bpm increase in heart rate from an already elevated resting rate. Next, the MqdEELV significantly increases by approximately 140 ml; and the M % RC even more significantly decreases, falling from an approximately normal level above 40% to a much depressed level below 16%. These changes are consistent with dynamic air trapping. Increasing MqdEELV reflects increased air trapping and decreased volumes available for ventilation. A significantly decreasing M % RC reflects further physiologically significant dynamic air trapping in an already hyperinflated lung. Additional trapped air in a hyperinflated lung both limits rib cage contraction and further decreases an already abnormally decreased lung compliance. Both factors reduce the mechanical efficiency of the rib cage muscles, and the decreasing M % RC indicates that these muscles are now incapable of the extra effort to maintain ventilation.

Table 1 further illustrates how the novel pattern of an increasing MqdEELV and a decreasing M % RC separates +DH from −DH patients.

TABLE 1 Patients Patients Parameter without DH with DH on (base line–end on exercise exercise Difference (+DH exercise) (−DH) (+DH) minus −DH) MqdEELV −81.36 66.50 −147.86 M%RC 1.09 −12.50 13.59 This table summarizes data from a study of fifteen patients, some with DH confirmed by standard measurement techniques and some without DH also as confirmed by standard techniques. Changes in MqdEELV and M % RC were measured for each patient. It is readily apparent that +DH patients exhibit the pattern of increasing MqdEELV and decreasing M % RC, while −DH patients exhibit other patterns.

In further preferred embodiments, values of MqdEELV and M % RC are combined using a model developed according to statistical pattern recognition techniques for particular patient populations into a discriminant variables that clearly distinguish +DH from −DH in each population. Further, these discriminant variables can be chosen so that amount of DH (in ml) correlates with the value of the variables so that both presence and amount of DH can be determined. Other embodiments use models developed by other than statistical techniques and can incorporate further variables (such as intensity of exercise or of other inciting cause).

Preferred Systems and Methods of this Invention

Preferred systems and methods suitable for the practice of this invention are described next.

This invention can be practiced in many different patient monitoring environments as long as respiratory data is available from which at least moment-by-moment V_(T) data and rib cage contribution to V_(T) can be determined. For example, this invention can be practiced in hospital, clinic, or laboratory environments and use data from respiratory sensors available in these environments. Such sensors include, e.g., spirometeric measuring arrangements, body plethysmography, and the like and are often less portable, can limit or prevent patient motion, but offer greater measurement accuracies. These environments also often provide, e.g., exercise treadmills and the like which can provide graded stimuli to precipitate DH. This invention can also be practiced in a patient's day-to-day environment while the patient is performing day-to-day activities (referred to herein as “ambulatory environments”). In such embodiments, this invention usually processes data from respiratory sensors that are portable, light weight, non-invasive, and is arranged and configured so as not to limit patient motion or activity. Although practice in ambulatory environments is preferred and further described herein, this should not be understood to limit the broad applicability of this invention.

For ambulatory environments, respiratory sensors preferably respond to indicia of body sizes, such as lengths, circumferences, diameters, or geometrically similar measures of at least the rib cage and the abdominal and of their moment-by-moment changes during respiration. As already described, from rib cage and abdominal size measurements, moment-by-moment V_(T) and rib cage contribution to V_(T) can be determined. Such sensors (referred as “size sensors”) at one or more additional torso or limb cross-sections can provide additional data responsive to cardiac or aortic pulsations, venous or arterial pulsations, and the like.

Size sensors can be based on diverse technologies, including magnetometers; strain gauges using magnetic, mechanical or optical means; optical techniques including interferometry; electrical impedance; surface electrical or magnetic activity; body plethysmography, ultrasonic and doppler measurements of body wall motions or body diameters; and so forth. Preferred size sensors are based on inductive plethysmographic (IP) technology, which is responsive to anatomic sizes by measuring the self-inductance of one or more conductive elements (metallic or non-metallic) arranged on the anatomic portion to be measured. Briefly, IP sensor self-inductance varies as its size changes in response to an underlying body part; the varying self-inductance is sensed by variable frequency oscillator/demodulator modules; these modules output digital signals responsive to oscillator frequencies and ultimately to snesor size. Respiratory measurements obtained by IP technology are routinely within 5-7% (or 10s of ml) of measurements obtained by spirometry, a current clinical standard.

IP technology has been described in patent applications and issued patents assigned to the assignee of the present application including: U.S. Pat. Nos. 6,551,252; 6,413,225; 6,047,203; 6,341,504; 5,331,968; 5,301,678; 4,807,640; 4,373,534; and 4,834,209, and also U.S. application Ser. No. 10/822,260. All patents and published U.S. application cited herein are incorporated herein by reference in their entireties for all purposes.

Additional sensors are advantageous in order to record the context when DH is found to occur. For example, accelerometers mechanically coupled to a monitored patient can provide data reflecting activity level and posture; sensors for blood oxygen saturation can provide data reflecting any de-saturation accompanying DH. Other sensors can provide data reflecting skin conductance, electrical impedances, temperature, sensors; ultrasound, and the like.

Respiratory and other sensor data is conveyed from the monitored patient to processing devices, or computers, or computer systems for processing and analysis by programmed implementations of this invention's methods. If patients are constrained, sensors can be linked directly to processing systems, e.g., by cable. If patients are unconstrained as in ambulatory environments, it is preferable that a portable processing device or computer (referred to as a “portable data unit” or “PDU”) carried by a subject receive sensor data. In certain embodiments, the PDU also temporarily stores and/or transmits it to remote computers for analysis. In an ambulatory environment, data transmission should not limit a subject and can be by, e.g., wireless transmission, or physical transport of computer readable media, or the like. In other embodiments, the PDU also perform the methods of this invention.

For ambulatory applications, respiratory and other sensors are preferably configured unobtrusively and comfortably on the patient so as not to substantially limit motion or activity. For example, sensors can be configured into one or more wearable items, e.g., shirts, jackets, bands, patches, and the like. FIGS. 6A-C illustrate exemplary ambulatory monitoring systems having sensors configured into wearable items.

The subject of FIG. 6A is actively exercising unconstrained by concurrent monitoring with a single chest band 71 and PDU 73 configured as a wrist watch. The single band preferably incorporates a size sensor sensitive to respiration and can also incorporate accelerometers, ECG sensors, temperature sensors, and so forth. FIG. 6B illustrates shirt-like garment 75 having several types of sensors, including two (or more) size sensors 77 preferably sensitive to rib cage (RC) and abdomen (AB) sizes, two ECG leads, and optionally additional sensors (not illustrated). Here, PDU 81 can displays data and accept user input. FIG. 6C illustrates a body-suit-like garment 83 equipped with a more extensive array of size sensors 85 for measuring respiration rate and volume, venous and arterial pulsations, cardiac pulsations, individual lung function, and the like. Here, PDU 87 is attached to the garment and retrieves and wirelessly transmits sensor data to storage and analysis systems. Monitoring garments and systems are available from VivoMetrics, Inc., Ventura, Calif.

Sensor data can be partially or wholly processed by a processing device in the PDU. In certain embodiments, processed and/or raw data is also transmitted to a remote computer system. FIG. 6D illustrates an exemplary analysis system including PC-type or workstation-type computer 91 with an attached monitor for viewing unprocessed and processing sensor data. Data is 89 conveyed to system 91 by, e.g., wireless connection, physical transfer, or wired connection. Local or remote online computer readable memory 93 and removable computer readable memories 95 (e.g., optical ROM) holds unprocessed and/or processed data and/or programs, and the like.

The methods of this invention are generally performed on a computer or other processing device. Accordingly, these methods are programmed in a convenient computer language, such as assembly language, C, or C++, compiled into an executable form and stored on a computer readable medium for loading into a program memory of a computer or the configuration registers of a programmable device, or the like. FIG. 7 illustrates an exemplary implementation of these methods. These illustrated steps can be repeated on demand, or intermittently, or periodically to make multiple DH assessments.

After beginning at step 101, next step 103 measures and/or retrieves and/or inputs current monitoring data and optionally comparative measurement data. DH is additional hyperinflation acutely superimposed on chronic, baseline (BL) hyperinflation. To assess the degree or severity of a current bout of DH, it is therefore advantageous to have available comparative data that measures of the BL hyperinflation. Comparative data can include values or combinations of MqdEELV and M % RC during past bouts of DH, statistical distributions of multiple observations of MqdEELV and M % RC; the values of MqdEELV and M % RC resulting from specific precipitation factors, the amount of air retained, and the like.

Should the input data include RC and AB size measurements, step 105 next extracts tidal volume (V_(T)) and the rib cage contribution to tidal volume from these raw methods accordingly to methods known for IP. Step 107 perform implementations of the previously methods for finding M % RC and MqdEELV. Finally, step 109 assesses DH, its presence or absence and optionally its amount or volume, from the determined M % RC and MqdEELV parameters accordingly to the discrimination already described. The severity of DH can be estimated from the increases in lung volume or other measured in view of past values provided in the comparative data.

Validation step 111 is optional but preferred to insure and/or improve the reliability of DH assessments. In one simple alternative, single measurements the M % RC and MqdEELV parameters are made from sufficiently long measurement periods, e.g., at least 30 sec, or at least 60 sec, or at least 120 sec. long or longer. In another alternative, DH assessment uses M % RC and MqdEELV values that are determined by statistically combining two or more independent measurements of these parameters. In a further alternative, a final DH assessment is determined by statistically combining values from two or more independent episodes of DH precipitated by separate occurrences of a precipitating factor, e.g., exercise. Values can be combined using modes, medians, averages, and the like in order to statistically improve accuracies and limit errors. Prior DH episodes can be provided in the comparative data.

The invention described and claimed herein is not to be limited in scope by the preferred embodiments herein disclosed, since these embodiments are intended as illustrations of several aspects of the invention. Any equivalent embodiments are intended to be within the scope of this invention. Indeed, various modifications of the invention in addition to those shown and described herein will become apparent to those skilled in the art from the foregoing description. Such modifications are also intended to fall within the scope of the appended claims.

A number of references are cited herein, the entire disclosures of which are incorporated herein, in their entirety, by reference for all purposes. Further, none of these references, regardless of how characterized above, is admitted as prior to the invention of the subject matter claimed herein. 

1. A computer-implemented method for assessing dynamic hyperinflation (DH) in a monitored subject, comprising: receiving respiratory data monitored with a physiological monitoring system during a current period of monitoring the subject, said monitoring system having at least one sensor, said respiratory, data including respiratory volumes during rib cage(RC) expansion and contraction; determining from said received data at one or more times during said current monitoring period a first parameter (M % RC) with processor means, said first parameter comprising median % rib cage, said first parameter (M % RC) reflecting relative contributions of expansions and contractions of the rib cage (RC) to tidal volumes (V_(T)) of the subject; determining from said received data at one or more times during said current monitoring period a second parameter (MqdEELV) with said processor means, said second parameter (MqdEELV) comprising median cumulative changes in end-expiratory lung volume; and assessing DH in the subject during said current monitoring period in dependence on said determined MqdEELV and M % RC parameters.
 2. The computer-implemented method of claim 1, wherein said current monitoring period comprises a plurality of sub-periods, and wherein said M % RC parameter and said MqdEELV parameter are determined for each of said plurality of sub-periods only from received respiratory data from that sub-period.
 3. The computer-implemented method of claim 1, wherein DH is present during said current monitoring period if M % RC parameter values increase and if MqdEELV parameter values decrease during said current monitoring period.
 4. The computer-implemented method of claim 1, wherein DH is present during said current monitoring period in comparison with a previous monitoring period if M % RC parameter values during said current monitoring period are greater than during said, previous monitoring period and if said MqdEELV parameter values during said current monitoring period are less than during said previous monitoring period.
 5. The computer-implemented method of claim 4, wherein said previous monitoring period comprises a baseline period during which DH is known to be absent.
 6. The computer-implemented method of claim 1, wherein said determination of said M % RC parameter comprises dividing said respiratory volumes during rib cage (RC) expansion and contraction by tidal volume (V_(T)).
 7. The computer-implemented method of claim 6, wherein said received data comprises rib cage (RC) sizes at one or more selected positions on the subject, and wherein said respiratory volumes during rib cage (RC) expansion and contraction are determined in dependence on one or more rib cage (RC) sizes.
 8. The computer-implemented method of claim 1, wherein determination of said MqdEELV parameter comprises determining a breath-by-breath change in expiratory lung volume (EELV) for a first period of time, determining a cumulative change in said EELV in dependence on plurality of consecutive breath-by-breath changes in said EELV, and determining said MqdEELV parameter in dependence on the absolute values of said plurality of cumulative changes in said EELV.
 9. The computer-implemented method of claim 8, wherein said determination of a breath-by-breath change in EELV comprises subtracting expiratory volume of a breath from inspiratory volume of said breath.
 10. The computer-implemented method of claim 1, wherein said received data comprises anatomical sizes at one or more positions on the subject's torso, said torso positions including one or more positions on the rib cage (RC) and one or more positions on the abdomen (AB), and wherein the method further comprises determining moment-by-moment lung volume parameters by linearly combining said rib cage (RC) and abdomen (AB) sizes, and determining tidal volume, (V_(T)) in dependence on a difference between a first lung volume parameter at the end of inspiration and a second lung volume parameter at end of an immediately following expiration.
 11. The method of claim 1, wherein the subject comprises a mammal.
 12. The method of claim 1, wherein the subject performs normal daily activities during the monitoring period.
 13. A computer for assessing dynamic hyperinflation (DH) in a monitored subject, comprising: a processor, and a computer-readable memory operatively coupled to said processor and configured with computer instructions that cause said processor to perform the steps of receiving comparative respiratory data monitored during a current period of monitoring, determining from said received data at one or more times during said monitoring period a first parameter (M % RC), said first parameter (M % RC) comprising median % rib cage, determining from said received data at one or more times during said monitoring period a second parameter (MqdEELV), said second parameter (MqdEELV) comprising median cumulative changes in end-expiratory lung volume, and assessing DH in the subject during said monitoring period in dependence on said determined MqdEELV and M % RC parameters.
 14. The computer of claim 13, wherein said processor and memory are sized and configured, whereby said processor and memory can be easily carried by the subject.
 15. A portable monitoring system for assessing dynamic hyperinflation (DH) in a monitored subject, comprising: a wearable item comprising one or more sensors that provide data comprising sizes at one or more selected positions on the subject's torso, said selected positions including one or more positions on the rib cage (RC) and one or more positions on the abdomen (AB), said selected positions being selected so that said torso sizes change with respiration; a portable data unit operably linked to said sensors, said portable data unit comprising a processing device and a computer memory operably linked to said processing device and configured with computer instructions that cause said processor to perform the steps of receiving sensor data monitored during a current period of monitoring, determining tidal volume (V_(T)) in dependence on a difference between a first moment-by-moment lung volume parameter at end of inspiration and a second lung volume parameter at end of an immediately following expiration, determining from said received data at one or more times during said monitoring period a first parameter (M % RC), said first parameter (M % RC) comprising median % rib cage, wherein determination of said M % RC parameter comprises dividing respiratory volumes due to rib cage (RC) expansion and contraction by tidal volume (V_(T)), determining from said received data at one or more times during said monitoring period a second parameter (MqdEELV), said second parameter (MqdEELV) comprising median cumulative changes in end-expiratory lung volume, wherein determination of said MqdEELV parameter comprises determining a breath-by-breath change in expiratory lung volume (EELV), determining a cumulative change in said EELV in dependence on a plurality of consecutive breath-by-breath changes in said EELV, and determining said MqdEELV parameter in dependence on absolute values of said plurality of cumulative changes in said EELV, and assessing DH in the subject during said monitoring period in dependence on said determined MqdEELV and M % RC parameters.
 16. The portable monitoring system of claim 15, wherein said wearable item comprises a garment and/or a shirt and/or a band.
 17. The portable monitoring system of claim 15, wherein said portable data unit is housed so as to be carried on said wearable item of clothing worn by the subject.
 18. The portable monitoring system of claim 15, wherein said sensors comprise one or more inductive plethysmographic sensors or one or more posture and/or activity sensors.
 19. The portable system of claim 15, wherein said portable data unit stores data on a removable computer-readable memory.
 20. The portable monitoring system of claim 15, wherein said portable data unit wirelessly transmits data to a remote computer.
 21. The computer-readable medium comprising encoded instructions for causing a processor to perform the method of claim
 1. 